1. Field of Invention
The present invention relates generally to a apparatus and method for detecting forged signatures on a check or draft.
2. Brief Description of Prior Art
Under principles set forth in the Uniform Commercial Code, financial institutions, such as banks and credit unions, are responsible for insuring that checks are validly authorized by the person to whom the drafts were originally issued. Payment on a forged draft results not in the debit of the account of the person to whom the drafts were issued, but rather of the account of the financial institution which honored the forged draft. If a forged draft is detected, the retail or service business that accepted the draft bears the brunt o r the loss.
Losses to financial institutions, and to retail and service businesses, resulting from forged drafts have a significant impact upon the consumer in terms of higher bank fees and higher retail/service prices. Until recently, the only method available to protect against forged signatures was to rely on manual signature checks to determine whether the signature on a draft was valid.
Manual signature checks suffer from many disadvantages, especially with respect to the time involved in conducting such checks. In a financial institution setting, a manual signature check generally entails the bank teller locating the signature card of the transactor from a compilation of thousands of cards and visibly comparing the signature on the card against that on the draft. Such comparison has traditionally only been made on high value single transaction drafts. In a retail or service sector setting, such comparison is generally made by obtaining a signed identity instrument from the consumer, such as a credit card, license etc., and comparing the signature thereon with that which is inscribed on the draft which has been tendered. Management approval is often sought prior to approval. Such attempts to reduce bad-draft losses often result in customer dissatisfaction due to inconvenience, delays, and embarrassment.
Because of the difficulties involved in manual comparisons, computer-assisted methods for ascertaining the validity of a signature have been introduced over the past 20 years. There are two basic approaches to computer-assisted handwriting identification: (1) comparison systems and methods whereby a stored reference signature and a specimen signature are displayed together to allow the validity of the specimen to be judged by the observer and (2) automatic signature verification systems and methods which process data from a reference signature and a specimen signature, and report whether or not the specimen is verified.
Many financial institutions today employ computer-assisted comparison systems and methods for verifying signatures, for example, the check imaging system of SQN. This choice over automated systems and methods is likely due to the high degree of dissatisfaction and failure rate which have accompanied prior automated signature verification systems. Typically, computer-assisted comparison systems provide a plurality of stored reference signatures on a screen against which the signature on a draft may be compared. The draft signature may be feed into the computer for visual comparison of "overlap" with the stored signatures. Systems such as described in U.S. Pat. No. 5,347,589 to Meeks provide reference signatures along with one or more dynamic handwriting parameters represented thereon to allow better visual comparison of the presented signature with the reference signature.
Automated verification methods and apparatuses can be broadly classified into two classes: (1) those involving verification of a signature at point-of-purchase, or "dynamic" verification and (2) those involving verification of a previously executed draft, or "static" verification.
Numerous patents have issued with respect to dynamic point-of-purchase verification systems. Such systems take into account variations in such unique handwriting parameters as applied pressures, or combination of applied pressures, direction and timing of movement of a stylus during the act of handwriting. By measuring and storing information with respect to the same in a database, point-of-purchase systems permit comparison of signatures written at the time of purchase with the database-stored information. U.S. Pat. Nos. 3,480,911, 3,563,097, 3,906,444, 3,956,734, 4,008,457, 4,040,010, 4,040,011, 4,040,012, 4,495,644, 4,553,258, 4,581,482, 4,789,934, 4,111,052, 4,128,829, 4,131,880, 4,308,522, 4,701,960, 4,856,077, 5,054,088, 5,109,426, 5,226,091, 5,422,959, and 5,434,928 all teach special apparatuses whereby the dynamic variations inherent in writing may be converted into electrical signals for analysis. Each requires the writer to use a special pen device to produce the handwriting specimen. None is capable of examining a signed document directly.
Automated static signature verification systems were first proposed as early as the late 1960s, albeit, significant commercial acceptability has yet to surface. Early systems relied essentially on character recognition and pattern super-position types of analysis methods based on x-y positional data. Such systems apparently failed owing to the inherent variability of such positional information. Later systems searching for more exacting methods of verification have employed digital imaging processing technology.
For example, U.S. Pat. No. 4,454,610 discloses a static system, comprised of an encoding and verification device, for classifying and identifying patterns which may be used to verify signatures. A submitted signature is imaged by a lens system to produce two optical images, each of the images are optically transformed, optically averaged and then converted into electrical signals by vidicon tubes. The signals are combined in such a way that the resultant set of signals is the Fourier transform of the transmitted signature. The logarithm of the resultant signals is taken and this final transform is displayed on a screen. A set of fixed detectors measure the intensity values of particular pixels on the screen and this set of values is used to classify/identify the submitted signature.
U.S. Pat. No. 4,985,928 describes a static signature verification system in which optical density measurements of a plurality of elements in a handwriting specimen are made. Density measurements which form continuous line segments of at least a predetermined length, and the location of the same, along the sample are recorded and processed and compared to a reference specimen. The signature is converted to a set of vectors which contain the average density reading across the width of the signature line and the X and Y coordinates of the center of the line. The vector points may be organized as a sequential set of points to permit the flagging of end and crossing points. A five-point smoothing algorithm is used to average out any minuscule imperfections ascertained in the signature thus accomplishing the averaging of two points on both sides of a point to perceive the density at the point being averaged so that both density and X and Y position may be read.
U.S. Pat. No. 5,257,320 describes a static signature verification system employing light analysis to produce component digital representations. A predetermined area on a carrier medium is illuminated and the light reflected therefrom converted to an electrical signal which is subsequently converted to black and white pixel representations of the sample signature. The pixel representations are analyzed to determine the number of pixels which satisfy a plurality of pre-selected features of the sample signature. The resultant determination is compared against that discerned from a plurality of reference signatures.
And further, U.S. Pat. No. 5,251,265 discloses a static signature verification system wherein the image of a signature is aligned with respect to a given axis and is then digitized such that there is provided a grey scale representation of each pixel. Several parameters are measured from the grey scale representations, including the center of gravity of the pixels within each row and column of the representation, a center of gravity line for the centers of gravity for the rows or columns, the center of gravity for all pixels of the image, the positions of maximum grey scale pixels within each row or column, sums of grey scale values per column or row and the shape of a bow within the signature image. Second order polynomials which describe unique segments of the signature are also determined. Such measurements are contrasted against the same measurements made on a plurality of reference signatures, and verification made on the basis of the correlation between the same.
A significant problem associated with static signature verification systems of the past was the need to determine a set of algorithms which were to apply to all sample signatures which were to be analyzed by the system. Extremely reliable algorithms have been found to be elusive, the latter being attributed to the wide-ranging variations which exist with respect to the manner in which individuals write their signatures. Recognition of this problem resulted in several groups proposing "neural network" software techniques, patterned after the striate cortex of a human being, for the computer to generate individualized signature algorithms with respect to a particular person's signature. Neural network software techniques for pattern recognition are described in U.S. Pat. No. 5,251,269.
Application of neural network technology has recently resulted in the commercial introduction of a static electronic signature verification system known as Chequematch.RTM., a product marketed by AEA Technology, a commercial division of the United Kingdom Atomic Energy Authority. Neural network software permits systems such as Chequematch to learn the unique characteristics of a signature from a plurality of reference signatures input into the system. The "learning" process is said to permit these systems to generate algorithms that more appropriately define the writing characteristics of the individual than prior systems.
A significant problem associated with all static verification systems, including neural network-based verification systems, is the complex manner in which they attempt to deal with or adapt to the variabilities inherent in signature-string calligraphy including the changing variables of penmanship and alterations of the habits of the signer. Penmanship variability relates to changes such as the particular type of instrument being used to sign the signature, the length of the signature line on the document (e.g., a signature fitted onto a small line may look substantially different from a signature fitted onto a large line), the position where the signer begins and ends the signature string, how the hand of the signer is positioned on the document being signed (such positioning can dramatically affect the slope nature of the signature particularly if the hand is repositioned midway in the signing process) and the type of substrate upon which the signature is made (e.g., one's signature on a piece of corrugated cardboard may look substantially different than one's signature on a piece of plain paper). Variability in signer habits is due to a multiplicity of factors including, illness or injuries (e.g., when ill, people frequently write smaller), the emotional state of the signer (e.g., the signature of a depressed person often shows a reduction in the size of letter extensions and crosses), and even simple things like the weather (e.g. one often signs things quicker, and thus in a less neat way, when trying to get out of the cold or rain). Changes in one's signature are particularly magnified when one looks at the signature of the same individual over a life time. Few adults continue to sign their name the same way they did when they were six or seven years of age. Similarly, the signature of an elderly person not infrequently significantly differs from the signature of the same individual when they were middle-aged. A signature algorithm may be inadequate for distinguishing authentic from invalid signatures even if it is based upon a plurality of exemplar signatures if such exemplar signatures were supplied on the same day, or in temporal proximity to one another.
Present day static verification systems, further suffer from the inherent complexity of the verification process. Determination of slope functions, pixel contrast, x-y coordinate positions of signature components, center of gravity measurements, the utilization of "smoothing" algorithms, require in-depth analysis of the entire signature string and comparison of these characteristics to the same characteristics, and associated variability of characteristics, determined from analysis of authenticate signature exemplars. As discussed supra, these systems often entail construction of complex algorithms based on a limited sample of authenticate signatures supplied by the person for whom signature comparison is sought. The complexity of the algorithms may lead to many false negatives occurring with respect to truly valid signatures, and in some cases may slow processing of signed documents to an unacceptable rate.
Therefore, there is a need for a static verification system employing a less complex verification protocol which takes into account the variable nature of a person's signing habits, which provides for reducing false negative rejections of authenticate signatures, that is minimally affected by the writing instrument used by the person signing the document, by the document stock and by the length of the signature line on the document to be signed, which is relatively unaffected by the manner in which the signer holds the writing instrument and begins/ends the signature, and yet permits efficient rejection of forged signatures.